{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IASI data availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, os, re, warnings\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import cudf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_datetime(filename):\n",
    "    try:\n",
    "        mat = re.search(r'(\\d{4}\\d{1,2}\\d{1,2})', filename)\n",
    "        s = mat.group()\n",
    "        fmt = '%Y%m%d'\n",
    "        return datetime.strptime(s, fmt)\n",
    "    except:\n",
    "        warnings.warn('Invalid input')\n",
    "\n",
    "def to_julian(filename):\n",
    "    try:\n",
    "        return to_datetime(filename).timetuple().tm_yday\n",
    "    except:\n",
    "        warnings.warn('Invalid date')\n",
    "        \n",
    "def lon_to_idx(lon, resolution = 1):\n",
    "    return math.trunc((180 + lon) / resolution)\n",
    "\n",
    "def lat_to_idx(lat, resolution = 1):\n",
    "    return math.trunc((90 + lat) / resolution)\n",
    "\n",
    "def container(resolution = 1):\n",
    "    width = round(360 / resolution)\n",
    "    height = round(180 / resolution)\n",
    "    return np.zeros(width * height).reshape(width, height)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Checking for missing measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_missing(year):\n",
    "    datadir = f'/home/ubuntu/Data/IASI/METOPA/NH3/{year}'\n",
    "    files = os.listdir(datadir)\n",
    "    files.sort()\n",
    "    files = list(filter(lambda x: 'nh3' in x, files))\n",
    "    d = dict()\n",
    "    last_day = to_julian(str(year) + '1231')\n",
    "    for i in range(1, last_day + 1):\n",
    "        d[i] = 0\n",
    "    for file in files:\n",
    "        d[to_julian(file)] += 1\n",
    "    for i in range(1, last_day + 1):\n",
    "        if d[i] < 2:\n",
    "            print(datetime(year, 1, 1) + timedelta(i - 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Data filtering\n",
    "\n",
    "Filtering criteria\n",
    "\n",
    "- column > 0\n",
    "- error < 50%\n",
    "- cloud coverage < 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_data(year):\n",
    "    datadir = f'/home/ubuntu/Data/IASI/METOPA/NH3/{year}'\n",
    "    filtered = []\n",
    "    files = os.listdir(datadir)\n",
    "    files.sort()\n",
    "    for file in files:\n",
    "        dt = to_datetime(file)\n",
    "        daynight = 'AM' if 'AM' in file else 'PM'\n",
    "        ds = xr.open_dataset(os.path.join(datadir, file))\n",
    "        df = ds.where(ds.error < 50).where(ds.column > 0).to_dataframe().dropna()\n",
    "        count = len(df)\n",
    "        df['year'] = [dt.year for i in range(count)]\n",
    "        df['month'] = [dt.month for i in range(count)]\n",
    "        df['day'] = [dt.day for i in range(count)]\n",
    "        df['daynight'] = [daynight for i in range(count)]\n",
    "        filtered.append(df)\n",
    "    return pd.concat(filtered)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "## How many valid observations are there during daytime and nighttime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": "/home/ubuntu/anaconda3/envs/rapids/lib/python3.7/site-packages/xarray/coding/variables.py:145: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n  condition |= data == fv\n"
    }
   ],
   "source": [
    "df = filter_data(2009)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = cudf.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "day = np.array([len(df[(df.daynight == 'AM') & (df.month == i)]) for i in range(1, 13)])\n",
    "night = np.array([len(df[(df.daynight == 'PM') & (df.month == i)]) for i in range(1, 13)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x7f48e0580a20>"
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": "<Figure size 432x288 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(1, 13), day, color = '#deb068', label = 'Day')\n",
    "plt.bar(np.arange(1, 13), night, bottom = day, color = '#43676b', label = 'Night')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Valid Observations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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